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Background: A smartphone is a promising tool for daily cardiovascularmeasurement and mental stress monitoring. A smartphone camera-basedPhotoPlethysmoGraphy (PPG) and a low-cost thermal camera can be used to createcheap, convenient and mobile monitoring systems. However, to ensure reliablemonitoring results, a person has to remain still for several minutes while ameasurement is being taken. This is very cumbersome and makes its use inreal-life mobile situations quite impractical.  Objective: We propose a system which combines PPG and thermography with theaim of improving cardiovascular signal quality and capturing stress responsesquickly.  Methods: Using a smartphone camera with a low cost thermal camera added on,we built a novel system which continuously and reliably measures two differenttypes of cardiovascular events: i) blood volume pulse and ii)vasoconstriction/dilation-induced temperature changes of the nose tip. 17healthy participants, involved in a series of stress-inducing mental workloadtasks, measured their physiological responses to stressors over a short windowof time (20 seconds) immediately after each task. Participants reported theirlevel of perceived mental stress using a 10-cm Visual Analogue Scale (VAS). Weused normalized K-means clustering to reduce interpersonal differences in theself-reported ratings. For the instant stress inference task, we built novellow-level feature sets representing variability of cardiovascular patterns. Wethen used the automatic feature learning capability of artificial NeuralNetworks (NN) to improve the mapping between the extracted set of features andthe self-reported ratings. We compared our proposed method with existinghand-engineered features-based machine learning methods.  Results, Conclusions: ... due to limited space here, we refer to ourmanuscript.

Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging